December 06, 2021
The filters learned by Convolutional Neural Networks (CNNs) and the feature maps these filters compute are sensitive to convolution arithmetic. Several architectural choices that dictate this arithmetic can result in feature-map artifacts. These artifacts can interfere with the downstream task and impact the accuracy and robustness. We provide a number of visual-debugging means to surface feature-map artifacts and to analyze how they emerge in CNNs. Our means help analyze the impact of these artifacts on the weights learned by the model. Guided by our analysis, model developers can make informed architectural choices that can verifiably mitigate harmful artifacts and improve the model’s accuracy and its shift robustness.
Written by
Bilal Alsallakh
Vivek Miglani
Shubham Muttepawar
Edward Wang (EcoF)
Sara Zhang
David Adkins
Orion Reblitz-Richardson
Publisher
NeurIPS Workshop
July 23, 2024
Zecheng He, Bo Sun, Felix Xu, Haoyu Ma, Ankit Ramchandani, Vincent Cheung, Siddharth Shah, Anmol Kalia, Ning Zhang, Peizhao Zhang, Roshan Sumbaly, Peter Vajda, Animesh Sinha
July 23, 2024
July 23, 2024
Llama team
July 23, 2024
July 21, 2024
Ouail Kitouni, Niklas Nolte, Samuel Pérez Díaz, Sokratis Trifinopoulos, Mike Williams
July 21, 2024
July 08, 2024
Antonio Orvieto, Lin Xiao
July 08, 2024
Product experiences
Foundational models
Product experiences
Latest news
Foundational models